Heartbeat Signals Research Articles

Application of nonlinear signal analysis in millimeter-wave radar blood flow rate monitoring

Abstract Due to the advantages of millimeter-wave radar, such as its relatively simple structure, small size, high sensitivity, etc., it is used to obtain the human pulse signal and blood flow signal. Using the methods of mean cancellation, phase differencing, and smoothing, static clutter removal and heartbeat signal enhancement can be achieved, which can effectively eliminate interference information. Based on the collected information, the three-dimensional Navier-Stokes equations and boundary conditions are determined, and the theoretical knowledge related to fluid mechanics and bioengineering is used to construct the model of the multidimensional blood monitoring system, and the nonlinear signal analysis method is used to analyze the application of the constructed model. Based on the data, there is only a 0.1% difference between the waveform of the blood vessel diameter measured by the multidimensional blood monitoring system and that of the blood vessel diameter tested by commercial ultrasound equipment. The waveform trend is also consistent. In addition, the blood flow velocity at point 5 was much greater than that at point 2, when the vessel stenosis exceeded 50%. This study can accurately and efficiently monitor blood flow velocity for single and multiple individuals, which is critical for the healthcare field's development and innovation.

A Comparative Study of Methods of Person Identification Using Radar-Extracted Heartbeat Signals

A Comparative Study of Methods of Person Identification Using Radar-Extracted Heartbeat Signals

Stretchable polymer optical fiber embedded in the mattress for respiratory and heart rate monitoring

In this study, a stretchable polymer optical fiber with a sandwich structure was proposed, which integrated a silicone tube filled with polydimethylsiloxane (PDMS) with two common commercial polymethylmethacrylate (PMMA) optical fiber. By integrating the stretchable polymer optical fiber into the designed sleep-monitoring mattress, the pressure sensitivity of the mattress to measure human respiratory rate, heart rate, and body motion information is improved. The weak respiratory and heartbeat signals were amplified by an amplifier circuit and the respiratory and heart rates were obtained by an algorithm. The experimental results indicated that the mattress can monitor respiratory rate and heart rate values in different positions as well as body movement information and can differentiate between three different behavioral states, including not on the bed, lying, and moving on the bed. In addition, clinical trials were conducted on 10 subjects, and the results were analyzed using the Bland-Altman analysis method, confirming the accuracy of the mattress, with maximum errors of less than 1 breath per minute and 2 beats per minute for respiration and heartbeat respectively. In addition, the mattress's reliability and stability were verified through continuous sleep monitoring at various times of day. The mattress has the advantages of low cost, softness, comfort, and high sensitivity, and is suitable as a home medical device for long-term continuous monitoring of respiratory and heart rates during sleep.

Noncontact Cardiac Activity Detection Based on Single-Channel ISM Band FMCW Radar.

The heart is an important organ that maintains human life activities, and its movement reflects its health status. Utilizing electromagnetic waves as a sensing tool, radar sensors enable noncontact measurement of cardiac motion, offering advantages over conventional contact-based methods in terms of comfort, hygiene, and efficiency. In this study, the high-precision displacement detection algorithm of radar is applied to measure cardiac motion. Experimental is conducted using a single out-channel frequency modulated continuous wave (FMCW) radar operating in the ISM frequency band with a center frequency of 24 GHz and a bandwidth of 150 MHz. Since the detection signal is influenced by both respiratory and heartbeat movements, it is necessary to eliminate the respiratory signal from the measurement signal. Firstly, the harmonic composition of the respiratory signal is analyzed, and a method is proposed to calculate the parameters of the respiratory waveform by comparing the respiratory waveform coverage area with the area of the circumscribed rectangle. This allows for determining the number of respiratory harmonics, assisting in determining whether respiratory harmonics overlap with the frequency range of the heartbeat signal. Subsequently, a more accurate cardiac motion waveform is extracted. A reference basis is provided for extracting cardiac health information from radar measurement waveforms by analyzing the corresponding relationship between certain extreme points of the waveform and characteristic positions of the electrocardiogram (ECG) signal. This is achieved by eliminating the fundamental frequency component of the heartbeat waveform to emphasize other spectral components present in the heartbeat signal and comparing the heartbeat waveform, the heartbeat waveform with the fundamental frequency removed, and the heartbeat velocity waveform with synchronized ECG signals.

Recovering fetal signals transabdominally through interferometric near-infrared spectroscopy (iNIRS).

Noninvasive transabdominal fetal pulse oximetry can provide clinicians critical assessment of fetal health and potentially contribute to improved management of childbirth. Conventional pulse oximetry through continuous wave (CW) light has challenges measuring the signals from deep tissue and separating the weak fetal signal from the strong maternal signal. Here, we propose a new approach for transabdominal fetal pulse oximetry through interferometric near-infrared spectroscopy (iNIRS). This approach provides pathlengths of photons traversing the tissue, which facilitates the extraction of fetal signals by rejecting the very strong maternal signal from superficial layers. We use a multimode fiber combined with a mode-field converter at the detection arm to boost the signal of iNIRS. Together, we can detect signals from deep tissue (>∼1.6 cm in sheep abdomen and in human forearm) at merely 1.1 cm distance from the source. Using a pregnant sheep model, we experimentally measured and extracted the fetal heartbeat signals originating from deep tissue. This validated a key step towards transabdominal fetal pulse oximetry through iNIRS and set a foundation for further development of this method to measure the fetal oxygen saturation.

A novel clustering method for complex signals and feature extraction based on advanced information-based dissimilarity measure

In this paper, a new dissimilarity measure for more accurate feature extraction and clustering is put forward. The method is proposed from the perspective of the weighted-probability distribution of dispersion patterns and their rank order statistics, where an effective quantization procedure is provided and the loss of information can be reduced. The proposed dissimilarity is applied in the multidimensional scaling (MDS) method to investigate simulated and reality-based signals. The comparative experiment shows that the clustering results of the proposed technique are clearer and more appropriate. State-of-the-art techniques and conventional methods are both included in the comparative experiments. In particular, for the heartbeat signals, it is discovered that the distribution of the weighted-probabilities of the dispersion patterns can discriminate subjects with different physiological conditions and exhibit visible changes of the subject’s dynamical features when aging and disease attacks are taken place, which can be regarded as a microscopic insight of the dynamical mechanisms.

A novel imbalanced dataset mitigation method and ECG classification model based on combined 1D_CBAM-autoencoder and lightweight CNN model

To address the problems of the imbalanced datasets, limited computing power, and memory capability of wearable devices, this paper proposes a new hybrid method for imbalanced dataset mitigation and a novel lightweight Convolutional Neural Network (CNN) framework for electrocardiogram(ECG) signal classification. First, data enhancement method is used by changing the amplitude of a small number of samples and combining with the weighted loss function in the deep learning model. Then, One-Dimensional Convolutional Block Attention Module (1D_CBAM) mechanism autoencoder model is proposed to compress the ECG heartbeat signals and extract the features of the ECG heartbeats. A modified one-dimensional lightweight CNN model based on MobileNet architecture is eventually used to categorize various arrhythmias utilizing the above features. Using the MIT-BIH arrhythmia dataset, the proposed model is tested by dividing the MIT Database(MITDB) into Dataset1 and Dataset2, in conjunction with suggestions from AAMI and other heartbeat signal classification methods. The experimental results show that the proposed imbalanced dataset mitigation method has higher performance than other methods in this paper, the proposed model has the most stable convergence and the lowest reconstruction loss (approximately 0.1) in the feature extraction process under the same conditions. During the classification process, the lightweight CNN model has approximately 4x fewer trainable parameters(Params: 12,788) and its performance [Dataset1: F1-Score: 96.24% ACC: 98.21%; Dataset2: F1-Score: 98.73% ACC: 99.61%] is comparable to the traditional CNN model [Dataset1: F1-Score: 96.98% ACC: 99.17%; Dataset2: F1-Score: 99.18% ACC: 99.72%].

Listen to Your Heart – Studying Heartbeat Detection and Emotional Intelligence

Abstract: Perception of emotions is at the basis of scientific literature about emotions and is considered a crucial emotional function in the Mayer and Salovey hierarchical model of Emotional Intelligence. The perception of bodily signals plays an important role in the perception of one’s own and others’ emotions. Thus, the first aim of this study was to verify if interoceptive ability, referred to as the ability to perceive bodily signals or autonomic self-perception, is related to individuals’ emotional abilities measured with the MSCEIT ( Mayer-Salovey-Caruso Emotional Intelligence Test, 2002 ). Results evidenced a positive relationship between total EI and experiential area scores (perceiving emotions and sensations) and interoceptive performance scores. A second aim was to investigate if the artificial augmentation of the cardiac perception improved performances in the same tasks. Results showed that the augmented perception of the heartbeat signal did not influence any performance in MSCEIT’s subtests. We concluded that there is a significant relationship between EI and interoceptive ability but that the artificial augmentation of the perception of bodily signals does not influence MSCEIT performances.

Smart cushion-based non-invasive mental fatigue assessment of construction equipment operators: A feasible study

Mental fatigue has a considerable influence on the completion of work as well as the safety and health of construction equipment operators who have been exposed to human–machine interaction for an extended period of time. Existing techniques of monitoring and measuring mental fatigue among construction equipment operators are either invasive or rely on subjective judgements, limiting their applicability on construction sites. Therefore, this paper developed a non-invasive smart cushion to evaluate the mental fatigue of construction equipment operators through real-time continuous monitoring of the heartbeat and respiration signals. A laboratory experiment was designed, and twelve participants were recruited to conduct the simulated excavator operation for data acquisition. First, wearable sensors were used as a reference to verify the monitoring accuracy of heartbeat and respiration signals. Excellent correlations were realized between the results of the two monitoring methods. Second, the relevant time domain and frequency domain features were extracted to construct a random forest classification model to discover the relationship between vital signs and subjectively reported mental fatigue status. The results demonstrated that the random forest model had a classification accuracy of 92%, better than other commonly used machine learning algorithms. A combination of heartbeat and respiration features is more effective than a single feature in identifying and classifying mental fatigue among construction equipment operators. The study's findings indicate that it is feasible to apply the smart cushion to identify and classify the mental fatigue of construction equipment operators, therefore contributing to the improvement of their health and safety.

Radar-Beat: Contactless beat-by-beat heart rate monitoring for life scenes

Background and objective:Heartbeat is a crucial vital sign, and radio-frequency technology can measure the micro-vibration of the body surface from a distance and extract the heartbeat signal, which can solve the compliance problem caused by wearable sensors. However, most existing works verify short-term controlled experiments and seldom consider the challenges posed by random body movements and posture changes during long-term monitoring in daily life scenarios. In this article, we propose a systematic solution called Radar-Beat, which enables accurate heartbeat monitoring using an mmWave FMCW radar device. Our goal is to promote the integration of contactless heartbeat monitoring into life scenarios. Methods:We proposes a sensitive body motion detection algorithm and an optimal Range-bin selection algorithm, which can automatically identify body motion and update the best heartbeat signal channel. Then, we construct a personalized heartbeat template for each signal segment and propose a global optimization model to improve the accuracy of heartbeat length estimation. Results:We use the synchronized ECG signal as the ground truth, results show strong agreement between Radar-Beat and synchrony ECG devices for heart rate and inter-beat interval (IBI) measurements in healthy subjects. In the heartbeat monitoring experiment for a total of 72 h and 56 min involving 11 participants, under a time coverage of 91.2%, the median error of IBI estimation was 12 ms. Conclusion:Radar-Beat has strong robustness to different individuals in different postures and positions. It can be deployed in a hospital bed or home to enable continuous heartbeat monitoring in an unobtrusive way.

Game Fun Prediction Based on Frequency Domain Physiological Signals: Observational Study.

Traditionally, the subjective questionnaire collected from game players is regarded as a primary tool to evaluate a video game. However, the subjective evaluation result may vary due to individual differences, and it is not easy to provide real-time feedback to optimize the user experience. This paper aims to develop an objective game fun prediction system. In this system, the wearables with photoplethysmography (PPG) sensors continuously measure the heartbeat signals of game players, and the frequency domain heart rate variability (HRV) parameters can be derived from the inter-beat interval (IBI) sequence. Frequency domain HRV parameters, such as low frequency(LF), high frequency(HF), and LF/HF ratio, highly correlate with the human's emotion and mental status. Most existing works on emotion measurement during a game adopt time domain physiological signals such as heart rate and facial electromyography (EMG). Time domain signals can be easily interfered with by noises and environmental effects. The main contributions of this paper include (1) regarding the curve transition and standard deviation of LF/HF ratio as the objective game fun indicators and (2) proposing a linear model using objective indicators for game fun score prediction. The self-built dataset in this study involves ten healthy participants, comprising 36 samples. According to the analytical results, the linear model's mean absolute error (MAE) was 4.16%, and the root mean square error (RMSE) was 5.07%. While integrating this prediction model with wearable-based HRV measurements, the proposed system can provide a solution to improve the user experience of video games.

Simulator of Heart Rate and Respiratory Rate for Monitoring Accuracy of Intelligent Sleep Monitoring Devices

Invent a simulator which provides a simulation of heart rate and respiratory rate to the intelligent sleep monitoring devices based on precision pressure sensors. The simulator was composed of control part and simulated silicone doll. The simulated silicone doll contains heartbeat simulator and breathing simulation airbag. Heartbeat and breathing combination pressure signal can be produced according to frequency set values. Frequencies of pressure signal of the simulator were compared with the monitoring results of intelligent sleep monitoring devices with known accuracy to verify the frequency accuracy of pressure signal of the simulator. Verified the repeatability and stability of the simulator with a stopwatch. The heart rate of the simulator was with in ±2 beats per minute of the monitoring results of intelligent sleep monitoring devices and the respiratory rate of the simulator was with in ±2 times per minute of the monitoring results. The repeatability and stability of the simulator was better than ±5% according to results with a stopwatch. It's practicable to use the simulator which provides a simulation of heart rate and respiratory rate to the accuracy test of the intelligent sleep monitoring devices based on precision pressure sensor.

An end-end arrhythmia diagnosis model based on deep learning neural network with multi-scale feature extraction.

This study presents an innovative end-to-end deep learning arrhythmia diagnosis model that aims to address the problems in arrhythmia diagnosis. The model performs pre-processing of the heartbeat signal by automatically and efficiently extracting time-domain, time-frequency-domain and multi-scale features at different scales. These features are imported into an adaptive online convolutional network-based classification inference module for arrhythmia diagnosis. Experimental results show that the AOCT-based deep learning neural network diagnostic module has excellent parallel computing and classification inference capabilities, and the overall performance of the model improves with increasing scales. In particular, when multi-scale features are used as inputs, the model is able to learn both time-frequency domain information and other rich information, thus significantly improving the performance of the end-to-end diagnostic model. The final results show that the AOCT-based deep learning neural network model has an average accuracy of 99.72%, a recall of 99.62%, and an F1 score of 99.3% in diagnosing four common heart diseases.

An Operator Theoretic Approach for Analyzing Sequence Neural Networks

Analyzing the inner mechanisms of deep neural networks is a fundamental task in machine learning. Existing work provides limited analysis or it depends on local theories, such as fixed-point analysis. In contrast, we propose to analyze trained neural networks using an operator theoretic approach which is rooted in Koopman theory, the Koopman Analysis of Neural Networks (KANN). Key to our method is the Koopman operator, which is a linear object that globally represents the dominant behavior of the network dynamics. The linearity of the Koopman operator facilitates analysis via its eigenvectors and eigenvalues. Our method reveals that the latter eigendecomposition holds semantic information related to the neural network inner workings. For instance, the eigenvectors highlight positive and negative n-grams in the sentiments analysis task; similarly, the eigenvectors capture the salient features of healthy heart beat signals in the ECG classification problem.

A Multi-Target Localization and Vital Sign Detection Method Using Ultra-Wide Band Radar.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE-K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD-WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO-SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Short-Time Remote Heart Rate Measurement Based on mmWave FMCW Radar Frame Structure

This paper presents a novel method to reduce measurement time for remote heart rate measurement using mmWave FMCW radar. The proposed method is based on the sampling theory and the frame structure of recent FMCW radar systems. The range displacement from heartbeat is measured by 60GHz FMCW radar with a fixed frame interval, 50ms. With additional chirp repetition in each frame, the heartbeat signal can be sampled with increased or decreased interval. By adopting three different sampling rates from a single radar measurement, the resolution of heart rate measurement is improved in a short time, 2.5s. The proposed method is evaluated with a commercial off-the-shelf FMCW radar system and three different human subject, for no respiration and natural status. To evaluate the performance of the proposed method, the heart rate result is compared with ECG sensor and conventional signal processing method. The results show that the proposed method estimates the heart rate with lower error and shorter measurement time.

Remote Respiratory and Cardiac Motion Patterns Separation with 4D Imaging Radars.

Radar-based noncontact physiological signals monitoring is meaningful for daily health monitoring, post-disaster rescue, and public security. This paper focuses on the theoretical and experimental study of noncontact respiratory and cardiac motion signals separation employing remote sensing by a four-dimensional (4D) imaging radar. To adaptively separate respiratory and cardiac motion patterns, we propose a variational mode separation (VMS) algorithm. VMS is established on optimizing a variational problem to separate different modes. It minimizes the energy overlap of the heartbeat and respiration signals as well as their harmonics with an equality constraint. Both simulation and real scene data results show that the proposed VMS algorithm is suitable for separating the weaker cardiac motion pattern from the strong respiratory motion pattern, restraining the influence of respiration harmonics on the heartbeat component. Also, the continuous remote monitoring of respiratory rate (RR) and heart rate (HR) by employing the proposed method in a real scene shows a strong consistency with the reference respiration belt and electrocardiogram (ECG). The root mean square error (RMSE) of RR and HR for the noncontact measurement are 0.13 breaths per minute (brpm) and 1.7 beats per minute (bpm), respectively.

Research on Power Outage Sensing Technology of Medium and Low Voltage Distribution Network Based on Deep Learning of Multi-source Data Fusion

Aiming at the application of multi-source data fusion in intelligent operations of medium and low voltage distribution network system, a deep learning algorithm based on multi-source data fusion is proposed in this paper. Firstly, combining the stop/restart signals of a large number of users’ digital devices with the existing measurement information of the grid, two indicators of heartbeat and handshake signals have been constructed. Secondly, the convolutional neural network is combined with the improved D-S evidence theory, and an improved depth learning algorithm is proposed. Finally, taking a community as an example, the proposed algorithm is applied in practice and analyzed by simulation. The test results show that the model has good convergence and robustness. It is more intuitive and comprehensive in power failure evaluation. It has a wide power failure perception range and high accuracy and improves the quality of power supply service.

Towards Nonintrusive and Secure Mobile Two-Factor Authentication on Wearables

Mobile devices are promising to apply two-factor authentication to improve system security. Existing solutions have certain limits of requiring extra user effort, which might seriously affect user experience and delay authentication time. In this paper, we propose PPGPass, a novel mobile two-factor authentication system, which leverages Photoplethysmography (PPG) sensors available in most wrist-worn wearables. PPGPass simultaneously performs a password/pattern/signature authentication and a physiological-based authentication. To realize both nonintrusive and secure, we design a two-stage algorithm to separate clean heartbeat signals from PPG signals contaminated by motion artifacts so that users do not have to deliberately keep their bodies still. In addition, to deal with noncancelable issues when biometrics are compromised, we design a repeatable and non-invertible method to generate cancelable feature templates as alternative credentials. We leverage the great power of Random Forest and Support Vector Data Description to detect adversaries and verify a user's identity. To the best of our knowledge, PPGPass is the first nonintrusive and secure mobile two-factor authentication based on PPG sensors. Extensive experiments demonstrate that PPGPass can achieve the false acceptance rate of 3.11% and the false recognition rate of 3.71%, which confirms its high effectiveness, security, and usability.

MmRH: Noncontact Vital Sign Detection With an FMCW mm-Wave Radar

Vital signs, such as respiration rate (RR) and heart rate (HR), are essential for human health status assessment. The radar could detect RR and HR in a noncontact manner by sensing the repetitive chest wall movement caused by cardiopulmonary activity, which is attractive due to the more comfortable experience and better privacy protection. However, as the chest wall displacement caused by heartbeat is much smaller than that caused by respiration, the weak heartbeat component may be easily overwhelmed by respiration harmonics, noise, and clutter, making it difficult to achieve accurate and reliable HR detection. In order to tackle this challenge, in this article, we design mmRH, a system that can accurately estimate RR as well as HR using a frequency-modulated continuous-wave (FMCW) millimeter-wave (mm-wave) radar, which consists of four functional modules. In the vital sign signal extraction module, the range bin corresponding to the body part with strong vital signs is determined to extract the vital sign signal, eliminating the clutter interference. The differential enhancement module is proposed to enhance the heartbeat component by the first-order temporal difference, reducing the impacts of respiration harmonics and noise on HR estimation. In the signal decomposition module, the respiration and heartbeat signals are separated via wavelet packet decomposition (WPD) to further suppress respiration harmonics and high-frequency noise of the heartbeat signal. In the vital sign rate reconstruction module, the sparse spectrum reconstruction (SSR) of vital sign rates is mapped to an adaptive filter and zero attracting sign exponentially forgetting least mean square (ZA-SEFLMS) algorithm is proposed to achieve high-resolution sparse spectrum for accurate and reliable RR and HR estimation. The effects of different settings, including the distance between the human subject and the radar, the sensor position, and the user heterogeneity, are investigated by extensive experiments. The results indicate that the designed mmRH could effectively suppress the respiration harmonics, noise, and clutter interference, and bring a significant improvement in RR and HR detection accuracy compared with existing methods.